Image quality of digital images may be assessed based on various aspects, such as brightness, sharpness, noise, chromatic aberration, optical distortion, and/or the like. In recent years, both pixel counts and pixel densities of digital image sensors in the various image capturing devices have considerably increased. With such increased pixel counts and pixel densities, the image capturing devices are now capable to capture digital images with an increased detail information. Such digital images, as compared with other images with less detail information, are sharper and thus, considered better in terms of image quality. In the following discussions, we may only refer to digital camera as an example of image capturing devices. The similar arguments shall apply to all image capturing devices, such as cameras, camcorders, smart phones, tablets, scanners, and/or the like.
The digital images may contain both structure information and detail information. The structure information may correspond to coarse-granularity information of the digital images. Examples of the structure information may include, but are not limited to, contour edges, luminance information, and chrominance information of one or more objects in the digital images. The detail information may correspond to fine-granularity information of the digital images. Examples of the detail information may include, but are not limited to, various textures of one or more objects in the digital images. For example, when the digital image is an image of a horse, the structure information may correspond to contour edges, luminance information, and chrominance of the horse. The detail information may include the textures of the hair strands and eyelashes of the horse.
Currently, the capability of the modern digital cameras to capture the digital images with high detail information are determined by various factors. Apart from the reduced pixel count and pixel density of one or more image sensors inside the digital camera, there may also be other contributing factors, such as optical limitation, motion blur, and/or image processing inside/outside the digital camera, which may limit the capability of the digital camera to capture the digital images with high detail information.
The optical limitation of the digital camera may limit its capability to capture the digital images with high detail information. Camera lens generally work as optical low pass filters and some high frequency detail information may not pass through the lens and reach the one or more image sensors. The maximum optical resolution allowed by the camera lens is generally smaller than the resolution of the one or more image sensors. Many digital cameras are also equipped with an optical low pass filter in front of the one or more image sensors to suppress Moire artifacts, which may be another important source of the optical limitation.
The motion blur may also affect the capability of the digital camera to capture the digital images with high detail information. The motion blur is generally caused by camera movements and/or object movements. When the motion blur occurs, the detail information of object inside the digital image is essentially low-pass filtered before even reaching the camera lens of the digital camera. Possible solutions to overcome the motion blur may include, but are not limited to, application of optical stabilization, usage of tripod, and/or reduction of shutter time.
The image processing inside/outside the digital camera may further affect its capability to capture the digital images with high detail information. Many image capturing devices, such as the digital cameras, camcorders, and mobile devices, are usually equipped with an image processing unit to convert data from the one or more image sensors to the digital images. The image processing unit may comprise multiple functional blocks and each functional block may perform one of a plurality of image processing functions, known in the art. Examples of such image processing functions may include, but are not limited to, denoising, demosaicing, gamma correction, color space conversion, chromatic aberration correction, optical distortion correction, compression, and/or the like. The functional blocks are generally arranged in a sequential order such that output of a current block is the input of the next block. Such image processing units may be referred to as, “the image processing pipeline”.
In certain scenarios, due to implementation of the various functional blocks in the image processing pipeline, detail information of the digital images and/or video frames may be degraded. Such a degradation of the image details may not be desirable. In an exemplary scenario, a denoising block may be implemented to suppress noises, such as a thermal noise, and thereby, improves the image and video quality. While the denoising block suppress the noises through a technique, such as, “smoothing”, some detail information may be lost during such “smoothing”. In another scenario, a demosaicing block may be implemented to recover full resolution color information, when the color imaging sensors only capture one color per pixel location and rely on information of nearby pixels to recover values of other colors. Such recovery process may be essentially a “smoothing” process, which may reduce detail information of the digital images.
Thus, it may be desirable that the image processing pipeline improves detail information loss of the digital images. There may be other factors, such as pixel count and density of image sensors, optical limitation, and motion blur, that may affect the image processing pipeline, but are not discussed here.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.